WSPML 2024 - 2024 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2024)

★2024 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2024)---Ei Compendex & Scopus—Call for paper
June 7-9, 2024｜online｜Website: www.wspml.org
★WSPML 2024 presents researchers, engineers, and academics with an unprecedented opportunity to associate and interact with some of the foremost experts in the field of Signal Processing and Machine Learning from around the globe. WSPML 2024 aims to be the must-visit conference of the year for the field, and will be the ideal forum for the exchange of ideas and innovations that promise to change the face of the industry. For researchers, engineers, or industry professionals, this conference could mark an important point in your career.
★Publication and Indexing
All accepted and registered full papers will be published conjunctly with other conference proceeding in the form of digital conference proceeding and submitted to major citation databases like EI Compendex, SCOPUS, Google Scholar etc. for reviewing and indexing. Excellent Papers from WSPML 2024 will be recommended to be submitted for publication in journal.
★Keynote Speakers
Prof. Michalis Matthaiou——Queen’s University Belfast, United Kingdom
Prof. Chik Patrick Yue——The Hong Kong University of Science and Technology, China
★Program Preview/ Program at a glance
June 7: Registration + Icebreaker Reception
June 8: Opening Ceremony+ KN Speech+ Technical Sessions
June 9: Technical Sessions+ Half day tour/Lab tours
★Paper Submission
Submit Via CMT: https://cmt3.research.microsoft.com/WSPML2024
★CONTACT US
Ms. Riva H. W. Wong
Email: inquiry-AT-wspml.org
Website: www.wspml.org
★Topics of Interest
Signal, Image and Video Processing
Digital Signal Processing
Feature Extractions
Image and Multidimensional Signal Processing
Image/Video Processing and Coding
Mining Images and Texture
Mining Images in Computer Vision
Multimodal Data Fusion
Graphical Models and Kernel Methods
Deep Learning, Machine Learning, Pattern Recognition, Artificial Intelligence
Learning Theory
Machine Learning Methods
Learning of Action Patterns
Neural Methods
Classification Techniques
Data-Driven Adaptive Systems
Data-Driven Models